[1]} uses imbalanced support sets to create a more realistic and challenging benchmark for meta-learning. The authors use random-shot tasks with randomly selected classes (way) and samples (shot), which replace the balanced task during meta-training and meta-evaluation. A similar idea is explored by [2]}, [3]}, and [4]} with the last two using a fixed number of classes (way). However, none of these works quantify the impact of class-imbalance on the FSL task nor the advantages of Random-Shot meta-training. [2]} explores a small range of class-imbalance in the support set. However, details into the effects of class-imbalance are lost when combined with task-distribution imbalance, making it challenging to attribute any performance changes caused by class-imbalance. [3]} explore a pure class-imbalance problem on the support set, but their analysis is limited to just two methods (their proposal and MAML). In [4]}, meta-learning is applied on aerial imagery, exploring step imbalance ranging from 5 to 140 samples per class (shot); however, only two FSL methods are compared (Prototypical Networks and their RF-MML method). Previous work provides limited insight into class-imbalance at the task level.
